360 research outputs found
A silent speech system based on permanent magnet articulography and direct synthesis
In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies
Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory Input
Articulatory information has been shown to be effective in improving the
performance of HMM-based and DNN-based text-to-speech synthesis. Speech
synthesis research focuses traditionally on text-to-speech conversion, when the
input is text or an estimated linguistic representation, and the target is
synthesized speech. However, a research field that has risen in the last decade
is articulation-to-speech synthesis (with a target application of a Silent
Speech Interface, SSI), when the goal is to synthesize speech from some
representation of the movement of the articulatory organs. In this paper, we
extend traditional (vocoder-based) DNN-TTS with articulatory input, estimated
from ultrasound tongue images. We compare text-only, ultrasound-only, and
combined inputs. Using data from eight speakers, we show that that the combined
text and articulatory input can have advantages in limited-data scenarios,
namely, it may increase the naturalness of synthesized speech compared to
single text input. Besides, we analyze the ultrasound tongue recordings of
several speakers, and show that misalignments in the ultrasound transducer
positioning can have a negative effect on the final synthesis performance.Comment: accepted at SSW11 (11th Speech Synthesis Workshop
Ultrasound-Based Silent Speech Interface Built on a Continuous Vocoder
Recently it was shown that within the Silent Speech Interface (SSI) field,
the prediction of F0 is possible from Ultrasound Tongue Images (UTI) as the
articulatory input, using Deep Neural Networks for articulatory-to-acoustic
mapping. Moreover, text-to-speech synthesizers were shown to produce higher
quality speech when using a continuous pitch estimate, which takes non-zero
pitch values even when voicing is not present. Therefore, in this paper on
UTI-based SSI, we use a simple continuous F0 tracker which does not apply a
strict voiced / unvoiced decision. Continuous vocoder parameters (ContF0,
Maximum Voiced Frequency and Mel-Generalized Cepstrum) are predicted using a
convolutional neural network, with UTI as input. The results demonstrate that
during the articulatory-to-acoustic mapping experiments, the continuous F0 is
predicted with lower error, and the continuous vocoder produces slightly more
natural synthesized speech than the baseline vocoder using standard
discontinuous F0.Comment: 5 pages, 3 figures, accepted for publication at Interspeech 201
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